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DOE OSTI2025

Leveraging explainable AI to characterize floating-point exceptions in linear solvers

Linear solver packages are central to many scientific, engineering, and machine learning applications. When floating-point exceptions occur in these solvers, e.g., division by zero or overflow, numerical results are compromised and become unreliable. Existing static and dynamic analysis tools can detect such exceptions, but they do not explain why the exceptions occur in terms of the solver inputs. Here, we present a study to characterize the inputs that cause numerical exceptions in linear solver packages. Our approach uses explainable AI (XAI) to find the most relevant characteristics of input matrices that explain the occurrence of exceptions in the solvers. Since training data in this domain is scarce, we perform extensive data gathering and data augmentation to obtain exception-inducing inputs. Our approach uses a repair strategy on the features blamed by XAI to validate that such features indeed explain the exceptions. We compare the LIME and SHAP XAI techniques using a dozen matrix features with three classifiers. We evaluate the approach on three widely used linear solver packages and find that some input characteristics can explain the occurrence of exceptions 100% of the time, in specific solvers and preconditioners.

Explainable AI↗

NASA NTRS1971

Layered 'recognition cone' networks that pre-process, classify, and describe.

A sequence of six types of pattern recognition system is examined. A program is described to illustrate some of the features developed. The first type (similar to many of the programs currently used) preprocesses by applying layers of local averaging and differencing transforms to smooth, fill in gaps and heighten contours, curves, and angles. It then applies a set of characterizers, each of which implies a set of names. The program chooses the single most high implied name. The second type combines the preprocessing transforms and the characterizers into a single operation of general type. Transforms build up a next representation of the input, while the characterizers imply the output name. The third type erases the distinction between a transform and an implication. Now all outputs are stored in the next transform layer. As the program averages information, its layers shrink, so that the system builds a cone of layers. When the program reaches the apex (a layer of only one cell that contains all the information), it chooses the single name with which it classifies the input. The fourth type is capable of choosing more than one name and, therefore, can both describe and classify the scene. The fifth type examines the interrelations among the set of names chosen. The sixth step can be taken to converse about the scene, developing an appropriate description in response to suggestions and queries. This allows the program to perform more computations and to look again on demand.

Uhr, L.↗

NASA NTRS1987

Frequency domain synthesis of optimal inputs for adaptive identification and control

The input design problem of selecting appropriate inputs for use in SISO adaptive identification and model reference adaptive control algorithms is considered. Averaging theory is used to characterize the optimal inputs in the frequency domain. The design problem is formulated as an optimization problem which maximizes the smallest eigenvalue of the average information matrix over power constrained signals, and the global optimal solution is obtained using a convergent numerical algorithm. A bound on the frequency search range required in the design algorithm has been determined in terms of the desired performance.

Fu, Li-Chen↗

NASA NTRS1989

Estimated performance and future potential of solar dynamic and photovoltaic power systems for selected LEO and HEO missions

Solar Photo Voltaic (PV) and thermal dynamic power systems for application to selected Low Earth Orbit (LEO) and High Eccentric Orbit (Energy) (HEO) missions are characterized in the regime 7 to 35 kWe. Input parameters to the characterization are varied corresponding to anticipated introduction of improved or new technologies. Comparative assessment is made between the two power system types utilizing newly emerging technologies in cells and arrays, energy storage, optical surfaces, heat engines, thermal energy storage, and thermal management. The assessment is made to common ground rules and assumptions. The four missions (space station, sun-synchronous, Van Allen belt and GEO) are representative of the anticipated range of multi-kWe earth orbit missions. System characterizations include all required subsystems, including power conditioning, cabling, structure, to deliver electrical power to the user. Performance is estimated on the basis of three different levels of component technology: (1) state-of-art, (2) near-term, and (3) advanced technologies. These range from planar array silicon/IPV nickel hydrogen batteries and Brayton systems at 1000 K to thin film GaAs with high energy density secondary batteries or regenerative fuel cells and 1300 K Stirling systems with ultra-lightweight concentrators and radiators. The system estimates include design margin for performance degradations from the known environmental mechanisms (micrometeoroids and space debris, atomic oxygen, electron and proton flux) which are modeled and applied depending on the mission. The results give expected performance, mass and drag of multi-kWe earth orbiting solar power systems and show how overall system figures of merit will improve as new component technologies are incorporated.

Bents, David J.↗

NASA NTRS1989

Estimated performance and future potential of solar dynamic and photovoltaic power systems for selected LEO and HEO missions

Solar photovoltaic and thermal dynamic power systems for application to selected low-earth-orbit (LEO) and high-earth-orbit (HEO) missions are characterized in the regime 7 to 35 kWe. Input parameters to the characterization are varied to correspond to anticipated introduction of improved or new technologies. A comparative assessment is made of the two power system types for emerging technologies in cells and arrays, energy storage, optical surfaces, heat engines, thermal energy storage and thermal management. The assessment is made to common ground rules and assumptions. The four missions (Space Station, sun-synchronous, Van Allen belt, and GEO) are representative of the anticipated range of multikilowatt earth-orbit missions. The results give the expected performance, mass and drag of multikilowatt earth-orbiting solar power systems and show how the overall system figure of merit will improve as new component technologies are incorporated.

Bents, David J.↗

DOE OSTI2022

Very small-scale, segregating-fluidized-bed experiments: A dataset for CFD-DEM validation and uncertainty quantification

IWe report fluidization experiments were conducted on a small scale and with a rapid response (short duration) to enable corresponding simulations at low-computational cost. Rise times are reported for four or fewer polyethylene particles (intruders) in an air-fluidized bed of ~5000 group D glass beads. Experimental inputs were completely characterized—particle properties, system dimensions and operating conditions—which is necessary for validating computational fluid mechanics (CFD)-discrete element method (DEM) including a comprehensive uncertainty quantification (UQ) analysis. Input uncertainties are reported as bounds or cumulative distribution functions of measured values. The staggering number of simulations required to complete a UQ analysis (~O[10 4 ] simulations corresponding to ~5 uncertain inputs) motivates this study. These segregating-bed experiments are designed to permit analogous CFD-DEM simulations to complete in less than a day on a single (~2.5 GHz) computational processor unit (CPU). Segregation times are reported for several operating conditions, intruder sizes, and initial configurations, providing a rich dataset for numerical model testing, validation and UQ.

42 ENGINEERING↗

NASA NTRS1990

A comparison of operationally determined atmospheric densities from satellite orbit solutions and the exospheric temperature from the Jacchia-Roberts model

Operational orbit determination by the Flight Dynamics Division at the Goddard Space Flight Center has yielded a data base of orbit solutions covering the onset of solar cycle 22. Solutions for nine satellites include an estimated drag adjustment parameter (rho sub 1) determined by the Goddard Trajectory Determination System (GTDS). The rho sub 1 is used to evaluate correlations between density variations and changes in the following: 10.7-centimeter wavelength solar flux (F sub 10.7), the geomagnetic index A sub p, and two exospheric temperatures (T sub c and T sub infinity) adapted from the Jacchia-Roberts atmospheric density model in GTDS. T sub c depends on the daily and 81-day centered mean F sub 10.7; T sub infinity depends on T sub c and the geomagnetic index K sub p values. The highest correlations are between density and T sub infinity. Correlations with T sub c and F sub 10.7 are lower by 9 and 10 percent, respectively. For most cases, correlations with A sub p are considerably lower; however, significant correlations with A sub p were found for some high-inclination, moderate-altitude orbits. Results from this analysis enhance the understanding of the drag model and the accommodation of atmospheric density variations in the operational orbit determination support. The degree of correlation demonstrates the sensitivity of the orbit determination process to drag variations and to the input parameters that characterize aspects of the atmospheric density model. To this extent, the degree of correlation provides a measure of performance for methods of selecting or modeling the thermospheric densities using the solar F sub 10.7 and geomagnetic data as input to the process.

Ward, D. T.↗

NASA NTRS1986

Analytical ultrasonics for evaluation of composite materials response. Part 2: Generation and detection

To evaluate the response of composite materials, it is imperative that the input excitation as well as the observed output be well characterized. This characterization ideally should be in terms of displacements as a function of time with high spatial resolution. Additionally, the ability to prescribe these features for the excitation is highly desirable. Various methods for generating and detecting ultrasound in advanced composite materials are examined. Characterization and tailoring of input excitation is considered for contact and noncontact, mechanical, and electromechanical devices. Type of response as well as temporal and spatial resolution of detection methods are discussed as well. Results of investigations at Virginia Tech in application of these techniques to characterizing the response of advanced composites are presented.

Duke, J. C., Jr.↗

DOE OSTI2021

Microbial Biomass in Soils Receiving Varying Levels of Organic Inputs - A Data Compilation

This dataset contains soil microbial biomass data compiled from published laboratory studies and field manipulations of organic inputs to soils. Each study included at least three levels of a given type of organic input applied to a given type of soil. Studies were selected from the literature that had experimentally manipulated organic input rates to soils using unburned, non-synthetic material (e.g. plant litters, manures, and composts). Fifty-five (55) studies, conducted from 1997 to October 2019, were chosen and compiled into a dataset with 358 observations from 96 combinations of a given organic material applied to a given soil in agricultural systems, laboratory incubations, forests, or grasslands. • Studies were selected when biomass measurements used the chloroform fumigation-extraction procedure followed by carbon analysis, or, in two instances, studies estimated microbial biomass using phospholipid fatty acid analysis. • Studies that used fewer than three input rates or characterized microbial biomass via substrate induced respiration were excluded. • The temporal and spatial aspects of the laboratory and field studies were not essential to the analyses (Craig et al., 2021) and were not included with the measurement data. These data were used to assess the relationship between organic input amounts and microbial biomass. Complete reference citations are provided if additional information is needed. The compiled data are provided in two .csv files: (1) soil treatments and microbial biomass data and (2) literature citations. The “Study” field is the cross reference between files. Also provided is the R code for reproducing the results in the related publication.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Development of simplified model for injection rate prediction of diesel injectors during transient and steady operation

Determining the injection rate of the diesel injector during the transient short injection operations is a critical matter to enhance the combustion process by improving air/fuel mixture formation. In this regard, numerous injection models have been suggested in the literature to predict the injection rate accurately. However, such algorithms have not precisely predicted the rate of injection (ROI) in transient short injection events. In addition, most of those models require many input parameters to characterize hydraulic and mechanical subsystems in the injector, which can make them inconvenient to tune for different injector specifications, in other words not suitable for universal usage. Therefore, our study aims to develop a simple injection model that can predict injection rate precisely in the short and long injection durations considering relatively fewer input parameters. The model traces the pressure variation inside the sac based on the bulk modulus theory to predict nozzle exit velocity, thus the injection rate of the injector. In the algorithm, the bulk modulus of fuel (E) and discharge coefficients of the nozzle hole and needle seat (C d,H and C d,N ) have been considered to vary rather than set constant during the injection process since such flow parameters would have a critical impact on the flow development at the transient stage, thus affecting the injection rate of diesel injectors. Here with this modelling strategy, the accuracy of the new model on ROI prediction was confirmed with acceptable model error for both short and long injection durations by validating against experimental results. To understand the effect of the new model scheme on ROI prediction, ROI results with the new model have been discussed by comparing it with the ROI results predicted with constant flow parameters.

42 ENGINEERING↗

DOE OSTI2024

A variance deconvolution estimator for efficient uncertainty quantification in Monte Carlo radiation transport applications

Monte Carlo simulations are at the heart of many high-fidelity simulations and analyses for radiation transport systems. As is the case with any complex computational model, it is important to propagate sources of input uncertainty and characterize how they affect model output. Unfortunately, uncertainty quantification (UQ) is made difficult by the stochastic variability that Monte Carlo transport solvers introduce. The standard method to avoid corrupting the UQ statistics with the transport solver noise is to increase the number of particle histories, resulting in very high computational costs. In this contribution, we propose and analyze a sampling estimator based on the law of total variance to compute UQ variance even in the presence of residual noise from Monte Carlo transport calculations. Here, we rigorously derive the statistical properties of the new variance estimator, compare its performance to that of the standard method, and demonstrate its use on neutral particle transport model problems involving both attenuation and scattering physics. We illustrate, both analytically and numerically, the estimator’s statistical performance as a function of available computational budget and the distribution of that budget between UQ samples and particle histories. We show analytically and corroborate numerically that the new estimator is unbiased, unlike the standard approach, and is more accurate and precise than the standard estimator for the same computational budget.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2026

Segmentation method comparison for residual fiber length measurement across tiled microscopy images

Fiber length distribution (FLD), in part, governs mechanical properties in discontinuous fiber composites, yet manual measurement methods limit the high-throughput characterization needed for materials design optimization. This study compares deep learning segmentation approaches for automated FLD measurement in large-field microscopy, evaluating how method choice affects the microstructural descriptors used in structure-property-processing relationships. A critical challenge is that high-resolution microscopy images (10,000×10,000 pixels) must be tiled for deep learning analysis, fragmenting fibers at boundaries. We demonstrate that segmentation method proves crucial for measurement accuracy. For example, instance segmentation with Slicing Aided Hyper Inference (SAHI) preserves individual fiber integrity across tiles while semantic segmentation prioritizes speed. Comparing against manual measurement of extracted carbon fibers, YOLOv11-SAHI matched manual ground truth (238 μm weighted mean) with 40x speedup (4.5 vs 167 minutes per image). U-Net provides rapid quantification although it is at the cost of reduced accuracy due only reliably measuring stand-alone fibers. Our comparative analysis reveals that instance segmentation with SAHI better preserves length measurements while semantic segmentation prioritizes speed, providing empirical guidance for method selection. The characterization provides essential inputs for mechanical property prediction models and inverse design workflows, accelerating composite materials development cycles.

Additive manufacturing↗

DOE OSTI2023

Hyperbolic string tadpole

Hyperbolic geometry on the one-bordered torus is numerically uniformized using Liouville theory. This geometry is relevant for the hyperbolic string tadpole vertex describing the one-loop quantum corrections of closed string field theory. We argue that the Lamé equation, upon fixing its accessory parameter via Polyakov conjecture, provides the input for the characterization. The explicit expressions for the Weil-Petersson metric as well as the local coordinates and the associated vertex region for the tadpole vertex are given in terms of classical torus conformal blocks. The relevance of this vertex for vacuum shift computations in string theory is highlighted.

Firat, Atakan Hilmi↗

DOE OSTI2022

HyMARC Seedling: Optimized Hydrogen Adsorbents via Machine Learning and Crystal Engineering

This final report is presented in two parts: In Part 1, the H 2 capacities of a diverse set of 918,734 metal-organic frameworks (MOFs) sourced from 19 databases is predicted via machine learning (ML). Using only 7 structural features as input, ML identifies 8,282 MOFs with the potential to exceed the capacities of state-of-the-art materials. The identified MOFs are predominantly hypothetical compounds having low densities (<0.31 g/cm3) in combination with high surface areas (>5,300 m2/g), void fractions ($0.90), and pore volumes (>3.3 cm3/g). The relative importance of the input features are characterized, and dependencies on the ML algorithm and training set size are quantified. The most important features for predicting H 2 uptake are pore volume (for gravimetric capacity) and void fraction (for volumetric capacity). The ML models are available on the web, allowing for rapid and accurate predictions of the hydrogen capacities of MOFs from limited structural data; the simplest models require only a single crystallographic feature. In part 2, ways to improve the poor powder packing density of MOFs is discussed. More specifically, a strategy that improves packing efficiency and volumetric hydrogen gas storage density dramatically through engineered morphologies and controlled-crystal size distributions is presented that holds promise for maximizing storage capacity for a given MOF. The packing density improvement, demonstrated for the benchmark sorbent MOF-5, leads to a significant enhancement of volumetric hydrogen storage performance relative to commercial MOF-5. System model projections demonstrate that engineering of crystal morphology/size or use of a bimodal distribution of cubic crystal sizes in tandem with system optimization can surpass the 25 g/L volumetric capacity of a typical 700 bar compressed storage system and exceed the DOE targets 2020 volumetric capacity (30 g/L). Finally, a critical link between improved powder packing density and reduced damage upon compaction is revealed leading to sorbents with both high surface area and high density.

08 HYDROGEN↗

DOE OSTI2023

WRF Output from 270m domain running simulation with no 3D morphology, NUDAPT 3D morphology, 100m resolution 3D morphology and 10m resolution 3D morphology

This is a group of four datasets that were run for an experiment testing the effect of the resolution and the coverage of 3D urban morphological inputs on meteorological output. This data is associated with the paper "Sensitivity of Urban Morphology to the Resolution of Urban Morphological Feature Inputs: Implications for Characterizing Urban Sustainability."

integrated multisector multiscale modeling↗

DOE OSTI2024

WRF Output from 270m domain running simulation with no 3D morphology, NUDAPT 3D morphology, 100m resolution 3D morphology and 10m resolution 3D morphology

integrated multisector multiscale modeling↗

NASA NTRS1972

Estimation of automobile-driver describing function from highway tests using the double steering wheel

The automobile-driver describing function for lateral position control was estimated for three subjects from frequency response analysis of straight road test results. The measurement procedure employed an instrumented full size sedan with known steering response characteristics, and equipped with a lateral lane position measuring device based on video detection of white stripe lane markings. Forcing functions were inserted through a servo driven double steering wheel coupling the driver to the steering system proper. Random appearing, Gaussian, and transient time functions were used. The quasi-linear models fitted to the random appearing input frequency response characterized the driver as compensating for lateral position error in a proportional, derivative, and integral manner. Similar parameters were fitted to the Gabor transformed frequency response of the driver to transient functions. A fourth term corresponding to response to lateral acceleration was determined by matching the time response histories of the model to the experimental results. The time histories show evidence of pulse-like nonlinear behavior during extended response to step transients which appear as high frequency remnant power.

Delp, P.↗

NASA NTRS1983

Modeling correlation with flight data

The molecular contamination flow model (space 2 program) predicts the induced environment of the space shuttle orbiter/payload on orbit as well as the induced gas flow between the orbiter and payload surfaces. This computer program relates the input parameters which characterize the time dependent status of the orbiter/payload to the required output parameters. An application of the model to data from space transportation system flights 1, 2, and 3 shows that the model correlates well for direct flow and for return flux (STS-2/H2O). Correlation for return flux from other molecular sources/species appears to be within expectations but more analysis is required. The space 2 model seems to be an adequate predictive tool.

Ehlers, H. K. F.↗